Data Mining and Business Intelligence (2170715)

Unit-1:  Overview and concepts Data Warehousing and Business Intelligence

Why reporting and Analysing data, Raw data to valuable informationLifecycle of Data - What is Business Intelligence - BI and DW in today’s perspective - What is data warehousing - The building Blocks: Defining Features - Data warehouses and data 1marts - Overview of the components - Metadata in the data warehouse - Need for data warehousing - Basic elements of data warehousing - trends in data warehousing.

Unit-2:  The Architecture of BI and DW

BI and DW architectures and its types - Relation between BI and DW - OLAP (Online analytical processing) definitions - Difference between OLAP and OLTP - Dimensional analysis - What are cubes? Drill-down and roll-up - slice and dice or rotation - OLAP models - ROLAP versus MOLAP - defining schemas: Stars, snowflakes and fact constellations

Unit-3:  Introduction to data mining (DM)

Motivation for Data Mining - Data Mining-Definition and Functionalities – Classification of DM Systems - DM task primitives - Integration of a Data Mining system with a Database or a Data Warehouse - Issues in DM – KDD Process

Unit-4:  Data Pre-processing

Why to pre-process data? - Data cleaning: Missing Values, Noisy Data - Data Integration and transformation - Data Reduction: Data cube aggregation, Dimensionality reduction - Data Compression - Numerosity Reduction - Data Mining Primitives - Languages and System Architectures: Task relevant data - Kind of Knowledge to be mined - Discretization and Concept Hierarchy

Unit-5:  Concept Description and Association Rule Mining

What is concept description? - Data Generalization and summarization-based characterization - Attribute relevance - class comparisons Association Rule Mining: Market basket analysis - basic concepts - Finding frequent item sets: Apriori algorithm - generating rules – Improved Apriori algorithm – Incremental ARM – Associative Classification – Rule Mining

Unit-6:  Classification and Prediction

What is classification and prediction? – Issues regarding Classification and prediction: Classification methods: Decision tree, Bayesian Classification, Rule based, CART, Neural Network Prediction methods: Linear and nonlinear regression, Logistic Regression Introduction of tools such as DB Miner /WEKA/DTREG DM Tools

Unit-7:  Data Mining for Business Intelligence Applications

Data mining for business Applications like Balanced Scorecard, Fraud Detection, Clickstream Mining, Market Segmentation, retail industry, telecommunications industry, banking & finance and CRM etc., Data Analytics Life Cycle: Introduction to Big data Business Analytics - State of the practice in analytics role of data scientists Key roles for successful analytic project - Main phases of life cycle - Developing core deliverables for stakeholders.

Unit-8:  Advance topics

Introduction and basic concepts of following topics. Clustering, Spatial mining, web mining, text mining, Big Data: Introduction to big data: distributed file system – Big Data and its importance, Four Vs, Drivers for Big data, Big data analytics, Big data applications. Algorithms using map reduce, Matrix-Vector Multiplication by Map Reduce. Introduction to Hadoop architecture: Hadoop Architecture, Hadoop Storage: HDFS, Common Hadoop Shell commands , Anatomy of File Write and Read., NameNode, Secondary NameNode, and DataNode, Hadoop MapReduce paradigm, Map and Reduce tasks, Job, Task trackers - Cluster Setup – SSH & Hadoop Configuration – HDFS Administering – Monitoring & Maintenance.

Sr.	Title	Author	Publication	Amazon Link
1	Data Mining Concepts and Techniques	J. Han, M. Kamber	Morgan Kaufmann
2	Data mining: Concepts, models, methods and algorithms	M. Kantardzic	John Wiley &Sons Inc.
3	Data Warehousing Fundamentals	Paulraj Ponnian	John Willey
4	Data Mining: Introductory and Advanced Topics	M. Dunham	Pearson Education
5	Data Mining for Business Intelligence:Concepts, Techniques, and Applications in Microsoft Office	G. Shmueli, N.R. Patel, P.C. Bruce	Wiley India